TY - JOUR
T1 - CLigOpt: controllable ligand design through target-specific optimization
AU - Li, Yutong
AU - da Costa Avelar, Pedro Henrique
AU - Chen, Xinyue
AU - Zhang, Li
AU - Wu, Min
AU - Tsoka, Sophia
N1 - Publisher Copyright:
© The Author(s) 2024. Published by Oxford University Press.
PY - 2024/9/1
Y1 - 2024/9/1
N2 - Motivation: A key challenge in deep generative models for molecular design is to navigate random sampling of the vast molecular space, and produce promising molecules that strike a balance across multiple chemical criteria. Fragment-based drug design (FBDD), using fragments as starting points, is an effective way to constrain chemical space and improve generation of biologically active molecules. Furthermore, optimization approaches are often implemented with generative models to search through chemical space, and identify promising samples which satisfy specific properties. Controllable FBDD has promising potential in efficient target-specific ligand design. Results: We propose a controllable FBDD model, CLigOpt, which can generate molecules with desired properties from a given fragment pair. CLigOpt is a variational autoencoder-based model which utilizes co-embeddings of node and edge features to fully mine information from molecular graphs, as well as a multi-objective Controllable Generation Module to generate molecules under property controls. CLigOpt achieves consistently strong performance in generating structurally and chemically valid molecules, as evaluated across six metrics. Applicability is illustrated through ligand candidates for hDHFR and it is shown that the proportion of feasible active molecules from the generated set is increased by 10%. Molecular docking and synthesizability prediction tasks are conducted to prioritize generated molecules to derive potential lead compounds. Availability and implementation: The source code is available via https://github.com/yutongLi1997/CLigOpt-Controllable-Ligand-Designthrough-Target-Specific-Optimisation.
AB - Motivation: A key challenge in deep generative models for molecular design is to navigate random sampling of the vast molecular space, and produce promising molecules that strike a balance across multiple chemical criteria. Fragment-based drug design (FBDD), using fragments as starting points, is an effective way to constrain chemical space and improve generation of biologically active molecules. Furthermore, optimization approaches are often implemented with generative models to search through chemical space, and identify promising samples which satisfy specific properties. Controllable FBDD has promising potential in efficient target-specific ligand design. Results: We propose a controllable FBDD model, CLigOpt, which can generate molecules with desired properties from a given fragment pair. CLigOpt is a variational autoencoder-based model which utilizes co-embeddings of node and edge features to fully mine information from molecular graphs, as well as a multi-objective Controllable Generation Module to generate molecules under property controls. CLigOpt achieves consistently strong performance in generating structurally and chemically valid molecules, as evaluated across six metrics. Applicability is illustrated through ligand candidates for hDHFR and it is shown that the proportion of feasible active molecules from the generated set is increased by 10%. Molecular docking and synthesizability prediction tasks are conducted to prioritize generated molecules to derive potential lead compounds. Availability and implementation: The source code is available via https://github.com/yutongLi1997/CLigOpt-Controllable-Ligand-Designthrough-Target-Specific-Optimisation.
UR - http://www.scopus.com/inward/record.url?scp=85203234943&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btae396
DO - 10.1093/bioinformatics/btae396
M3 - Article
C2 - 39230708
AN - SCOPUS:85203234943
SN - 1367-4803
VL - 40
SP - ii62-ii69
JO - Bioinformatics
JF - Bioinformatics
ER -